Research on Raw Material Ordering Model and Transportation Scheme Based on Linear Planning

Abstract

This paper mainly focuses on the ordering and transportation of raw materials of a production enterprise. The linear programming model is established to optimize the ordering and transportation plan under the problem, which can help the production enterprise to order and transport the raw materials better. This paper first defines the characteristics of suppliers, through analysis and calculation and arrangement is divided into three parts: credit index, create value and supply, and by using the entropy weight method three indices’ weights assignment, into the equation of grey association analysis, it is concluded that the grey relation analysis as its importance score, then descending order, it is concluded that the top 50 most important suppliers. In this paper, the problems studied were decomposed into three sub-questions, and models were established to solve them respectively. For solving the minimum supplier, 0-1 variable was introduced to represent supplier selection, and the minimum supplier was the objective function, which could be expressed as:$\min \sum\limits_{i = 1}^{50} {{y_i}} $ To choose the best scheme, the use of SPSS for time series analysis, for problem 1 choose the importance of the top 50 suppliers to predict future supply 24 weeks, in order to forecast and need to complete orders index as a constraint, and the most economic optimization model is established based on this, the objective function of the model can be represented as:$\min 1.2 \cdot \sum\limits_{i = 1}^{50} {{x_{i1}}} + 1.1 \cdot \sum\limits_{i = 1}^{50} {{x_{i2}}} + \sum\limits_{i = 1}^{50} {{x_{i3}}} $ For the third sub-question, as for the second sub-question, SPSS was used for time series analysis to obtain the loss rate prediction of the transporter in the next 24 weeks. The minimum loss was taken as the objective function, which can be expressed as:$\min \sum\limits_{i = 1}^8 {\sum\limits_{j = 1}^3 {{m_i} \cdot {z_{ij}}} } $ After solving problem 2, the optimal ordering and transportation scheme is obtained. According to the analysis, the most economical optimization model calculates that the total cost consumption in the next 24 weeks will be 19173 units less than before, and the loss will be 19.1% less than before.